[{"data":1,"prerenderedAt":651},["ShallowReactive",2],{"uc-next-best-action-for-advisors":3,"uc-regulations":450},{"useCase":4,"evidence":200,"blitsAiDeployments":339,"benchmarks":340,"indicative":347,"related":350,"indexability":448,"includeUnpublished":207},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":23,"channels":27,"audience":30,"autonomy":31,"adoptionStage":32,"segment":33,"problem":34,"problemStats":35,"howItWorks":36,"valueDrivers":37,"kpis":41,"indicativeValue":48,"macroEstimates":81,"feasibility":82,"implementation":95,"risk":139,"blitsAi":176,"faq":178,"related":188,"datePublished":195,"dateModified":195,"lastVerified":195,"changelog":196,"slug":199},"AI next best action prompts for wealth advisors","Advisor next best action","AI next best action for wealth advisors","AI next best action engines give wealth advisors a ranked list of client prompts. In 2025, UBS said 80% of its US advisors actively used its STAAT Insights engine.","published","An AI engine for wealth advisors, not customers, that scans an advisor's whole book and surfaces a short, ranked list of client specific prompts, such as idle cash, a maturing deposit, a concentration to review, a life event or an early sign of attrition, each with the reasoning and data behind it, for the advisor to act on or dismiss.",[12,13,14,15],"advisor insights engine","relationship manager nudges","book of business opportunity alerts","wealth client prompts",[17,18],"wealth-and-asset-management","banking",[20,21,22],"sales","marketing","analytics-and-reporting",[24,25,26],"recommendation-and-personalization","prediction-and-scoring","content-generation",[28,29],"internal-tools","email","employee-facing","assist","early-adopters","front-office","An advisor responsible for a large book of clients cannot watch every account every day. The signals are\nthere (cash building up after a sale, a deposit about to mature, a portfolio drifting away from its\nmandate, a client who has stopped logging in), but they sit in different systems and surface too\nlate, often when the client has already moved money or called a competitor.\n\nDashboards do not solve it: they show everything and prioritize nothing. What advisors need is a\nshort list each morning of the few clients worth calling, why, and what to say, with the freedom\nto ignore a prompt that does not fit what they know about the client.",[],"1. **Collect signals.** Holdings, cash flows, maturities, product usage, service contacts,\n   portfolio alignment and permitted external data are gathered per client.\n2. **Score and rank.** Predictive models and business rules score opportunities and risks (for\n   example propensity to invest idle cash, risk of attrition), and a ranking step picks the few that\n   matter most for each advisor.\n3. **Explain.** A language model turns each prompt into a short rationale and suggested talking\n   points, citing the data behind it and, where relevant, the house view or an approved product.\n4. **Advisor decides.** The advisor acts, snoozes or dismisses the prompt, and the feedback is\n   used to improve the ranking.\n5. **Controls on the way out.** Any product recommendation that follows still goes through the\n   firm's suitability and product governance checks.",[38,39,40],"revenue-growth","employee-productivity","customer-experience",[42,43,44,45,46,47],"conversion-rate-uplift","revenue-uplift","churn-reduction","employee-adoption","hours-saved","time-saved-per-task",{"referenceOrg":49,"inputs":50,"formula":76,"currency":77,"period":78,"resultLabel":79,"caveat":80},"A wealth manager with 50,000 advised clients",[51,56,63,69],{"key":52,"label":53,"low":54,"high":54,"unit":52,"note":55},"clients","Advised clients",50000,"The reference firm.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"promptsActed","Prompts acted on per client per year",0.2,0.5,"prompts per client per year","Editorial assumption, replace with your own advisor capacity and pilot data.",{"key":64,"label":65,"low":66,"high":59,"unit":67,"note":68},"conversion","Share of acted prompts that lead to new business",0.1,"fraction of acted prompts","Editorial assumption. No public source on this page states a conversion rate for advisor prompts.",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75},"revenuePerWin","Annual revenue per converted prompt",300,1000,"USD per conversion per year","Editorial assumption, for example fees on newly invested cash. Replace with your own margins.","clients * promptsActed * conversion * revenuePerWin","USD","per year","Additional annual revenue from acted prompts","Gross revenue before cannibalization, costs and the business that advisors would have won anyway. Measure it with a control group of advisors or clients before relying on it.",[],{"complexity":83,"complexityNote":84,"dataPrerequisites":85,"integrations":90},"high","The explanation layer is easy; the signals are not. It needs a clean client data model across banking, investment and CRM systems, models that are validated and monitored, fairness testing, and adoption work so advisors trust the prompts.",[86,87,88,89],"Client holdings, transactions and cash flows across accounts","Product maturities, mandates and model portfolios","CRM activity, service contacts and prior prompt outcomes","Consent and marketing preference data per client",[91,92,93,94],"Portfolio management and core banking systems","CRM and advisor desktop","Data platform for features and model scoring","Suitability and product governance engine for any resulting recommendation",{"steps":96,"guardrails":112,"humanInTheLoop":119,"kpisToInstrument":120,"failureModes":126},[97,100,103,106,109],{"title":98,"detail":99},"Start with a handful of high value signals","Pick three to five prompts with clear value and simple logic (idle cash above a threshold, maturing deposits, large inflows) before building propensity models.",{"title":101,"detail":102},"Put the reason on every prompt","Each prompt shows the data that triggered it and a suggested talking point. Advisors ignore prompts they cannot explain to a client.",{"title":104,"detail":105},"Measure against a control group","Hold out a random group of clients or advisors and compare outcomes, so the revenue story survives scrutiny from finance and risk.",{"title":107,"detail":108},"Close the feedback loop","Capture act, snooze and dismiss with a reason, and retrain or retune rankings on that feedback every cycle.",{"title":110,"detail":111},"Test for fairness and conduct risk","Check that prompts do not systematically favor higher fee products or neglect client segments, and review any prompt type that pushes a product.",[113,114,115,116,117,118],"Prompts inform the advisor; nothing is sent to a client automatically","Any product recommendation passes the suitability and product governance checks","Contact respects marketing consent and preferences","Fairness and conflict of interest review of prompt types and rankings","Logged rationale for every prompt shown, with the data used","Prompt and action data are not used to rate individual advisors","The advisor decides whether and how to act on each prompt and owns the resulting advice. Business and risk owners approve new prompt types, and model risk validates the scoring models.",[121,122,123,124,125],"Prompt action rate and dismiss reasons per prompt type","Conversion and revenue against a control group","Client attrition in treated versus control books","Weekly active advisors using the prompts","Complaints or suitability exceptions linked to acted prompts",[127,130,133,136],{"title":128,"detail":129},"Prompt fatigue","Too many low value prompts and advisors stop looking. Cap the list and retire prompt types with low action rates.",{"title":131,"detail":132},"Product push dressed as insight","Rankings optimize for revenue and drift toward high margin products. Add conduct review and suitability checks.",{"title":134,"detail":135},"Unexplainable scores","Advisors cannot tell a client why they called. Show the triggering data with every prompt.",{"title":137,"detail":138},"Credit shown as credit decision","A prompt suggests a loan based on a score that is really a creditworthiness assessment, which brings high risk obligations. Keep credit decisions in the regulated credit process.",{"euAiAct":140,"regulations":143,"guidance":151,"controls":169,"incidents":175},{"tier":141,"basis":142},"context-dependent","Ranking investment and service prompts for an advisor is not listed in Annex III. It becomes high risk if the system evaluates the creditworthiness of natural persons, for example to decide which clients are offered lending (Annex III point 5(b)), so keep credit decisions out of the prompt engine. It is also high risk if the system itself is used to monitor or evaluate advisors' performance and behaviour, for example by scoring or ranking advisors on how they act on prompts (Annex III point 4(b)), so keep adoption reporting separate from performance management.",[144,145,146,147,148,149,150],"eu-ai-act","gdpr","uk-consumer-duty","mas-ai-risk-management","us-sr-11-7","iso-42001","mifid-ii",[152,158,164],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"ESMA public statement on the use of AI in the provision of retail investment services","European Securities and Markets Authority","europe","https://www.esma.europa.eu/sites/default/files/2024-05/ESMA35-335435667-5924__Public_Statement_on_AI_and_investment_services.pdf","Expects firms to act in the client's best interest when AI shapes recommendations, and to govern algorithmic bias and data quality.",{"title":159,"issuer":160,"region":161,"url":162,"note":163},"Principles to Promote Fairness, Ethics, Accountability and Transparency (FEAT)","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/publications/monographs-or-information-paper/2018/feat","Fairness principles apply to models that decide which customers receive which prompts or offers.",{"title":165,"issuer":166,"region":155,"url":167,"note":168},"Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","Sets high standards of consumer protection across financial services and requires firms to put their customers' needs first, which applies to advisor prompts that lead to a sale to retail customers.",[170,171,172,173,174],"Model inventory entries and validation for scoring models","Fairness and conflict of interest testing of rankings per segment","Logging of every prompt, its rationale and the advisor's action","Suitability check on any recommendation that results from a prompt","Periodic review of prompt types by business, risk and compliance",[],{"howToBuild":177},"On Blits.ai the scoring stays in the firm's own models or data platform; Blits.ai adds the\nexplanation and delivery layer. An **agentic workflow** runs on a schedule, reads scored signals\nand client context through **custom functions** (REST or SQL) or a **SQL knowledge base**, and an\n**AI agent** writes each prompt's rationale and talking points with **structured output**,\ngrounded in the house view held in a **knowledge base**.\n\nPrompts reach advisors by **email** or through the API inside the advisor desktop, and advisors\ncan ask a **Microsoft Teams** assistant about a client. **Agentic tasks** can watch for\nconditions such as a maturity date and fire a prompt when it is reached. Output **guardrails**\nwith policies the firm writes can block prompts that push products outside policy, and the\nworkflow keeps an **audit trail** of every run. Act, snooze and dismiss on each prompt is\ncaptured and used to improve the ranking.",[179,182,185],{"question":180,"answer":181},"Does next best action for advisors actually get used?","Where it is built into the daily workflow, it can be. In a December 2025 Financial Planning interview about UBS's US wealth management unit, UBS's chief data and analytics officer said 80% of advisors were actively using the STAAT Insights engine. In March 2023 Morgan Stanley listed its Next Best Action engine among its recent AI projects, describing it as an internally built engine that delivers timely, customized messages to clients and prospects, guided by the financial advisor.",{"question":183,"answer":184},"How do you prove the revenue effect?","With a control group. Compare treated and untreated advisors or clients over the same period, because the clients an engine flags are often the ones advisors would have called anyway.",{"question":186,"answer":187},"Is this a high risk AI system?","Not for investment and service prompts. It becomes high risk under the EU AI Act if it assesses the creditworthiness of individuals, or if the system itself is used to monitor or evaluate advisors' performance and behaviour, so credit decisions should stay in the regulated credit process and prompt data out of performance reviews.",[189,190,191,192,193,194],"wealth-advisor-knowledge-assistant","client-meeting-notes-and-crm-update","offers-and-rewards-agent","suitability-assessment-assistant","portfolio-drift-monitoring-and-rebalancing","insurance-broker-and-agent-assistant","2026-09-27",[197],{"date":195,"note":198},"First published","next-best-action-for-advisors",[201,232,265,286,313],{"title":202,"useCases":203,"organization":205,"vendors":210,"summary":213,"stage":214,"year":215,"channels":216,"languages":217,"metrics":219,"outcomeDisclosed":207,"sources":220,"verification":226,"grade":229,"id":230,"organizationSlug":231},"Citi Wealth: AskWealth assistant and Advisor Insights",[189,204,199],"investment-research-summarization",{"name":206,"anonymized":207,"country":208,"region":209,"industry":17},"Citi",false,"US","global",[211],{"name":206,"role":212},"in-house","Citi Wealth launched two AI tools built by its Data, Analytics and Innovation team. AskWealth is a generative AI assistant that gives service teams, advisors and managers answers across the wealth business, so that advisors can reach market insights and research when clients ask questions; after a launch in Asia it became available to Citi Wealth colleagues worldwide. Advisor Insights is a dashboard of timely messages about market moves, portfolios and events, including Chief Investment Office insights, piloted with Citigold and Citi Private Client advisors in North America with a wider rollout planned for Q4 2025 and Q1 2026. Citi says the tools will save hours of time but published no figures.","production",2025,[28],[218],"en",[],[221],{"url":222,"title":223,"publisher":224,"date":225},"https://www.citigroup.com/global/news/press-release/2025/citi-wealth-launches-advisor-insights-askwealth","Citi Wealth Launches \"Advisor Insights\" Pilot and \"AskWealth,\" AI-Driven \"Gamechangers\" for Client Communications","Citigroup","2025-08-25",{"level":227,"checkedAt":228},"source-verified","2026-09-26","B","citi-wealth-askwealth-and-advisor-insights","citi",{"title":233,"useCases":234,"organization":235,"vendors":238,"summary":240,"stage":241,"year":215,"channels":242,"languages":243,"metrics":244,"outcomeDisclosed":253,"sources":254,"verification":262,"grade":229,"id":263,"organizationSlug":264},"UBS: STAAT Insights engine for US financial advisors",[199],{"name":236,"anonymized":207,"country":208,"region":237,"industry":17},"UBS","north-america",[239],{"name":236,"role":212},"UBS's US wealth management business runs STAAT Insights, a machine learning engine from its Smart Technologies and Advanced Analytics Team (STAAT) that surfaces client opportunities and alerts to financial advisors, such as shifting liquidity needs from a maturing CD, a concentrated stock position or a life event, and sends pre meeting client briefings with suggested talking points. In a December 2025 interview its chief data and analytics officer said 80% of US advisors actively use the engine. Two time saving figures UBS has published are about its AI tools in general, not STAAT Insights alone, so they are not recorded as metrics here: an advisor recruiting page says some advisors using UBS AI have saved three to four hours per client meeting, and the same executive conservatively estimated that US advisors save 10,000 hours a month by using AI to prepare for client meetings.","scaled",[28],[218],[245],{"kpi":45,"value":246,"unit":247,"qualifier":248,"period":249,"claimant":250,"quote":251,"sourceUrl":252},80,"percent","exact","US advisors actively using STAAT Insights","organization","Let me give you some stats here: 80% of them are actively using that STAAT Insights engine.","https://www.financial-planning.com/news/ubs-turns-to-ai-to-gain-wallet-share-find-new-clients",true,[255,258],{"url":256,"title":257,"publisher":236},"https://www.ubs.com/us/en/wealth-management/financial-advisor-experience/articles/ai-for-financial-advisors.html","UBS AI for Financial Advisors: A New Standard in Growth",{"url":252,"title":259,"publisher":260,"date":261},"UBS turns to AI to gain wallet share, find new clients","Financial Planning","2025-12-01",{"level":227,"checkedAt":195},"ubs-staat-insights-for-advisors",null,{"title":266,"useCases":267,"organization":268,"vendors":270,"summary":272,"stage":214,"year":273,"channels":274,"languages":275,"metrics":276,"outcomeDisclosed":207,"sources":277,"verification":283,"grade":229,"id":284,"organizationSlug":285},"Morgan Stanley: Next Best Action engine for financial advisors",[199],{"name":269,"anonymized":207,"country":208,"region":237,"industry":17},"Morgan Stanley",[271],{"name":269,"role":212},"Morgan Stanley Wealth Management built Next Best Action, an internal AI based engine that delivers timely, customized messages to clients and prospects, guided by the financial advisor. In March 2023, when it announced a strategic initiative with OpenAI to create a bespoke solution that its financial advisors would use, the firm listed it among its recent AI projects, alongside its Genome capability that uses data analytics and machine learning to personalize client communication. No outcome figures are published in that release.",2023,[28],[218],[],[278],{"url":279,"title":280,"publisher":269,"date":281,"archivedUrl":282},"https://www.morganstanley.com/press-releases/key-milestone-in-innovation-journey-with-openai","Key Milestone in Innovation Journey with OpenAI","2023-03-14","https://web.archive.org/web/2026/https://www.morganstanley.com/press-releases/key-milestone-in-innovation-journey-with-openai",{"level":227,"checkedAt":195},"morgan-stanley-next-best-action","morgan-stanley",{"title":287,"useCases":288,"organization":290,"vendors":293,"summary":300,"stage":214,"year":301,"channels":302,"languages":303,"metrics":304,"outcomeDisclosed":207,"sources":305,"verification":310,"grade":311,"id":312,"organizationSlug":264},"CIMB Niaga: AI agents that help staff give proactive, life stage guidance",[199,289],"goal-based-financial-planning-assistant",{"name":291,"anonymized":207,"country":292,"region":161,"industry":18},"CIMB Niaga","ID",[294,297],{"name":295,"role":296},"Google Cloud","platform",{"name":298,"role":299},"Artefact","integrator","CIMB Niaga, one of Indonesia's largest banks, built purpose built AI agents with its AI Center of Excellence and Artefact on Google Cloud. The agents help bank staff offer tailored advice and proactive guidance matched to a customer's financial goals and life stage. No outcome figures were published.",2026,[28],[],[],[306],{"url":307,"title":308,"publisher":295,"date":309},"https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders","1,302 real-world gen AI use cases from the world's leading organizations","2026-04-22",{"level":227,"checkedAt":195},"C","cimb-niaga-proactive-guidance-agents",{"title":314,"useCases":315,"organization":316,"vendors":318,"summary":320,"stage":214,"year":215,"channels":321,"languages":322,"metrics":323,"outcomeDisclosed":253,"sources":331,"verification":336,"grade":311,"id":337,"organizationSlug":338},"J.P. Morgan: Coach AI for private client advisers",[189,199],{"name":317,"anonymized":207,"country":208,"region":237,"industry":17},"JPMorgan Chase",[319],{"name":317,"role":212},"J.P. Morgan's private client advisers use an internal generative AI tool, Coach AI, to find research and content for client conversations more quickly. The firm's asset and wealth management chief executive credited its AI tools, which pull clients' trading patterns and anticipate their questions, with helping advisers respond to clients during the April 2025 market sell off. Its asset and wealth management chief information officer said advisers find the right information up to 95% faster. The firm's aim of growing adviser client books by 50% over three to five years is a target, not a result.",[28],[218],[324],{"kpi":325,"value":326,"unit":247,"qualifier":327,"period":328,"claimant":250,"quote":329,"sourceUrl":330},"search-time-reduction",95,"up-to","time to find information for a client conversation","Our advisers are finding the right information up to 95% faster - which means they spend less time searching and more time engaging in meaningful conversations with clients","https://www.aol.com/news/jpmorgan-says-ai-helped-boost-170825172.html",[332],{"url":330,"title":333,"publisher":334,"date":335},"JPMorgan says AI helped boost sales, add clients in market turmoil","Reuters via AOL","2025-05-05",{"level":227,"checkedAt":228},"jpmorgan-coach-ai-advisers","jpmorgan-chase",0,[341],{"kpi":45,"label":342,"unit":247,"aggregate":253,"higherIsBetter":253,"n":343,"nUpTo":339,"median":246,"min":246,"max":246,"byClaimant":344,"vendorOnly":207,"points":345},"Employee adoption",1,{"organization":343,"vendor":339,"regulator":339,"independent":339},[346],{"evidenceId":263,"organization":236,"value":246,"qualifier":248,"claimant":250,"grade":229,"pooled":253},{"low":348,"high":349},300000,5000000,[351,367,391,406,419,432],{"slug":189,"title":352,"shortTitle":353,"definition":354,"status":9,"industries":355,"functions":356,"patterns":359,"audience":30,"autonomy":31,"adoptionStage":362,"segment":33,"evidenceCount":363,"publicEvidenceCount":363,"organizations":364,"bestGrade":229,"headline":264,"lastVerified":228,"indexable":253},"AI knowledge assistant for wealth advisors and relationship managers","Advisor knowledge assistant","A conversational assistant that answers a wealth advisor's or relationship manager's questions in seconds from the firm's own research, house view, product documentation and policies, with every answer linked to the source document so the advisor can check it before using it with a client.",[17,18],[357,20,358],"knowledge-management","customer-service",[360,361],"rag-knowledge-assistant","conversational-agent","mainstream",6,[365,206,317,269,236,366],"Bank of America","Yes Bank",{"slug":190,"title":368,"shortTitle":369,"definition":370,"status":9,"industries":371,"functions":372,"patterns":375,"audience":30,"autonomy":379,"adoptionStage":362,"segment":33,"evidenceCount":363,"publicEvidenceCount":363,"organizations":380,"bestGrade":229,"headline":385,"lastVerified":195,"indexable":253},"AI meeting notes and CRM update for wealth advisors","Advisor meeting notes","An AI notetaker for wealth advisors that turns a client advice meeting, recorded with the client's consent, into the file note, follow up message and CRM record the firm needs to evidence its advice; unlike a general meeting summarizer, its output becomes part of the regulated client record. It drafts a structured note with the client's goals, circumstances, decisions and action items, and writes it into the CRM once the advisor has approved it.",[17,18],[20,373,374],"regulatory-compliance","operations",[376,377,378,26],"summarization","speech-analytics","agentic-workflow","copilot",[365,381,269,382,383,384],"Commerzbank","Quilter","SEB","UniSuper",{"kpi":386,"label":387,"unit":247,"n":343,"nUpTo":339,"kind":388,"value":389,"qualifier":248,"claimant":390,"organization":383,"vendorReported":253},"productivity-gain","Productivity gain","reported",15,"vendor",{"slug":191,"title":392,"shortTitle":393,"definition":394,"status":9,"industries":395,"functions":397,"patterns":398,"audience":399,"autonomy":400,"adoptionStage":362,"segment":33,"evidenceCount":401,"publicEvidenceCount":402,"organizations":403,"bestGrade":229,"headline":264,"lastVerified":195,"indexable":253},"AI agent for personalized offers and rewards","Offers and rewards","A customer facing AI agent for banks and card issuers that picks the offer, reward or loyalty action most relevant to each customer at each moment from their transactions and context, delivers it in the app, in messaging or through a colleague, and helps the customer understand, track and redeem rewards in conversation. Unlike campaign personalization, it works inside the customer's own account and loyalty relationship, one moment at a time.",[18,396],"payments",[21,20,358],[24,25,361],"customer-facing","autonomous",8,3,[365,404,405],"Commonwealth Bank of Australia","DBS Bank",{"slug":192,"title":407,"shortTitle":408,"definition":409,"status":9,"industries":410,"functions":411,"patterns":413,"audience":30,"autonomy":379,"adoptionStage":415,"segment":33,"evidenceCount":416,"publicEvidenceCount":416,"organizations":417,"bestGrade":229,"headline":264,"lastVerified":228,"indexable":253},"AI assistant for investment suitability assessment and reports","Suitability assessment","An AI assistant that checks whether a proposed product or portfolio fits a client's risk tolerance, objectives, knowledge, experience and financial situation against the firm's rules, flags mismatches, and drafts the suitability rationale and report for the advisor to confirm, while hard rule failures are decided by deterministic checks, not by the model.",[17,18],[373,20,412],"risk-management",[378,26,414],"classification-and-routing","emerging",2,[269,418],"Vanguard",{"slug":193,"title":420,"shortTitle":421,"definition":422,"status":9,"industries":423,"functions":424,"patterns":425,"audience":427,"autonomy":379,"adoptionStage":415,"segment":428,"evidenceCount":429,"publicEvidenceCount":402,"organizations":430,"bestGrade":229,"headline":264,"lastVerified":195,"indexable":253},"AI portfolio drift monitoring and rebalancing proposals","Drift and rebalancing","Continuous monitoring of every client portfolio against its mandate or model, which detects drift beyond agreed bands and prepares a tax aware, low turnover rebalancing proposal with its rationale for an advisor or portfolio manager to approve before any trade is placed.",[17,18],[374,412,22],[426,378,25,26],"anomaly-detection","back-office","middle-office",4,[269,431,418],"SimCorp",{"slug":194,"title":433,"shortTitle":434,"definition":435,"status":9,"industries":436,"functions":438,"patterns":439,"audience":30,"autonomy":31,"adoptionStage":32,"segment":440,"evidenceCount":441,"publicEvidenceCount":441,"organizations":442,"bestGrade":229,"headline":264,"lastVerified":195,"indexable":253},"AI assistant for insurance brokers and agents","Broker and agent assistant","An AI assistant for tied agents, independent brokers, advisors and the insurer's own distribution staff that answers product, underwriting and process questions from approved sources, prepares personalized customer engagement and follow ups, validates and prioritizes leads, and drafts meeting notes and emails, so producers spend more time with customers.",[437],"insurance",[20,357],[360,24,26,376],"distribution",5,[443,444,445,446,447],"Manulife","Prudential plc","Sun Life","Waterdrop","Zurich Insurance Group",{"indexable":253,"reasons":449},[],[451,457,462,468,475,481,488,492,497,504,511,516,523,529,535,540,547,553,559,565,571,577,583,587,592,599,605,610,615,622,628,634,640,645],{"id":144,"label":452,"issuer":453,"region":155,"url":454,"description":455,"useCases":456,"indexable":253},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":145,"label":458,"issuer":453,"region":155,"url":459,"description":460,"useCases":461,"indexable":253},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",180,{"id":149,"label":463,"issuer":464,"region":209,"url":465,"description":466,"useCases":467,"indexable":253},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":469,"label":470,"issuer":471,"region":237,"url":472,"description":473,"useCases":474,"indexable":253},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":476,"label":477,"issuer":453,"region":155,"url":478,"description":479,"useCases":480,"indexable":253},"dora","DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":482,"label":483,"issuer":484,"region":155,"url":485,"description":486,"useCases":487,"indexable":253},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":146,"label":489,"issuer":166,"region":155,"url":167,"description":490,"useCases":491,"indexable":253},"FCA Consumer Duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":147,"label":493,"issuer":160,"region":161,"url":494,"description":495,"useCases":496,"indexable":253},"MAS AI risk management guidelines","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":498,"label":499,"issuer":500,"region":161,"url":501,"description":502,"useCases":503,"indexable":253},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":505,"label":506,"issuer":507,"region":209,"url":508,"description":509,"useCases":510,"indexable":253},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":148,"label":512,"issuer":513,"region":237,"url":514,"description":515,"useCases":510,"indexable":253},"SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":517,"label":518,"issuer":519,"region":155,"url":520,"description":521,"useCases":522,"indexable":253},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":524,"label":525,"issuer":526,"region":209,"url":527,"description":528,"useCases":389,"indexable":253},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":530,"label":531,"issuer":453,"region":155,"url":532,"description":533,"useCases":534,"indexable":253},"eu-amlr","EU Anti Money Laundering Regulation","https://eur-lex.europa.eu/eli/reg/2024/1624/oj","Regulation (EU) 2024/1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":536,"label":537,"issuer":453,"region":155,"url":538,"description":539,"useCases":534,"indexable":253},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":541,"label":542,"issuer":543,"region":237,"url":544,"description":545,"useCases":546,"indexable":253},"us-bsa","Bank Secrecy Act","FinCEN","https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":548,"label":549,"issuer":453,"region":155,"url":550,"description":551,"useCases":552,"indexable":253},"eu-accessibility-act","European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",12,{"id":554,"label":555,"issuer":556,"region":237,"url":557,"description":558,"useCases":552,"indexable":253},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":560,"label":561,"issuer":562,"region":209,"url":563,"description":564,"useCases":552,"indexable":253},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":566,"label":567,"issuer":453,"region":155,"url":568,"description":569,"useCases":570,"indexable":253},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":572,"label":573,"issuer":574,"region":237,"url":575,"description":576,"useCases":570,"indexable":253},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https://www.fcc.gov/consumers/guides/stop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":578,"label":579,"issuer":160,"region":161,"url":580,"description":581,"useCases":582,"indexable":253},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":150,"label":584,"issuer":453,"region":155,"url":585,"description":586,"useCases":582,"indexable":253},"MiFID II","https://eur-lex.europa.eu/eli/dir/2014/65/oj","Directive 2014/65/EU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":588,"label":589,"issuer":453,"region":155,"url":590,"description":591,"useCases":582,"indexable":253},"eu-psd2","PSD2","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":593,"label":594,"issuer":595,"region":155,"url":596,"description":597,"useCases":598,"indexable":253},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":600,"label":601,"issuer":602,"region":237,"url":603,"description":604,"useCases":401,"indexable":253},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",{"id":606,"label":607,"issuer":453,"region":155,"url":608,"description":609,"useCases":401,"indexable":253},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",{"id":611,"label":612,"issuer":453,"region":155,"url":613,"description":614,"useCases":363,"indexable":253},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":616,"label":617,"issuer":618,"region":619,"url":620,"description":621,"useCases":441,"indexable":253},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":623,"label":624,"issuer":625,"region":155,"url":626,"description":627,"useCases":429,"indexable":253},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":629,"label":630,"issuer":631,"region":155,"url":632,"description":633,"useCases":429,"indexable":253},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":635,"label":636,"issuer":637,"region":161,"url":638,"description":639,"useCases":402,"indexable":253},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":641,"label":642,"issuer":453,"region":155,"url":643,"description":644,"useCases":402,"indexable":253},"eu-mar","EU Market Abuse Regulation","https://eur-lex.europa.eu/eli/reg/2014/596/oj","Regulation (EU) 596/2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":646,"label":647,"issuer":648,"region":237,"url":649,"description":650,"useCases":402,"indexable":253},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",1790598302954]